基于改进A*算法的一种智能车路径规划方法

轨迹规划是智能车安全行驶的关键技术。本文基于A*算法在复杂地图轨迹规划耗时长，拐点多等问题，提出了一种基于图论及几何方法的改进A*算法的避障与导航方法。该方法在传统A*算法的基础上结合图论进行路径规划，同时剔除路径中冗余节点，并采用Labview进行具体的仿真实验来验证轨迹规划算法的性能。结果表明：该算法在复杂环境中仍能有效找到距离短且平滑路径，提高了智能车的运行效率降低了能耗，可用于实际的智能车安全行驶管理中。

Intelligent Vehicle Trajectory Planning based on Improved Genetic Algorithm

Trajectory planning is the key technology of intelligent vehicle safe driving. In order to solve the problems of long time consuming and many inflection points of A star algorithm in complex map trajectory planning, an improved A star algorithm based on the graph theory and the geometric method is proposed. The improved algorithm is combined the traditional A star algorithm with the graph theory, eliminate the redundant nodes, and the performance of the trajectory planning algorithm is verified by simulation experiments. The results show that the short distance and smooth path can still be found effectively in the complex environment, which improves the running efficiency of the intelligent vehicle and reduces the energy consumption, and can be used in the actual safe driving management of the intelligent vehicle.